For more than two centuries, each industrial revolution, from steam power and mechanization to electrified factories and the rise of computing, has promised to reshape how goods are made and moved. Today, a fourth revolution is well underway. Industry 4.0 is centered around smart robotics, the Internet of Things (IoT), and AI, and it’s no longer just a buzzword. McKinsey is tracking more than 100 organizations that are implementing these technologies and seeing significant operational improvements.
While automation, connectivity, and advanced analytics have already transformed what’s possible, they have also exposed a new kind of friction: disconnected information between retailers and manufacturers. That disconnect isn’t coming from the machines, but the information flowing through them.
The continued promise of Industry 4.0 depends on signal coherence. Before organizations invest in more sensors or smarter robots, they need to sync the data and information being collected across the value chain. Without that alignment, even the most sophisticated systems will optimize the wrong inputs.
The double-edged sword of data
Today’s supply chains generate more data than any industrial network in history. IoT analytics estimates that the total number of global connected IoT devices will grow from 21.1 billion in 2025 to approximately 39 billion by 2030. A typical retailer juggles hundreds of thousands of SKUs across thousands of locations, with data streaming in daily from point-of-sale (POS) systems, e-commerce orders, promotions, suppliers, and logistics operations.
When those signals are clear and connected, the results are powerful. AI models can blend POS data, promotions, and local events to anticipate demand changes. Probabilistic inventory models balance service and cost, while automation can optimize production schedules and logistics execution in minutes. Organizations using AI in their supply chains report throughput increases of 10-20%, inventory reductions of 15-30%, and measurable improvements in on-time, in-full performance.
But the more data that is generated, the more consequential incoherence becomes.
Every part of the supply chain depends on accurate, timely, and consistent signals such as what consumers are buying, what retailers expect to sell, how much factories need to produce, and what suppliers must deliver. When any part of those is misaligned, it’s a chain reaction that causes the entire system to lose efficiency. Even the latest IoT gadgets installed in the factory will not help. Factories end up signal rich and sensing poor.
For the sales signal to get to the shop floor, retailers and manufacturers need to collaborate. But there is the friction between retailers and manufacturers which often begins with simple disconnects.
For example, a retailer running a promotion may expect a sharp increase in demand and order more stock, while the manufacturer, working off an outdated forecast, produces too little or ships too late. One location ends up overstocked, while another is understocked, resulting in unnecessary costs for both sides.
One of the most common disconnects is the gap between what consumers actually purchase and what retailers plan to order — the sell-out and sell-in forecasts. When those two diverge, the manufacturer’s production plans quickly fall out of sync with real demand. Research from the IHL Group found that the global retail industry loses $1.73 trillion each year due to stockouts and overstocks. Even the most advanced automation cannot correct for that.
Building a foundation around unified planning
Solving these disconnects requires more than new tools; it demands a new operating model. Unified planning provides that foundation by connecting demand, inventory, production, and logistics into a single continuous loop.
Each function adjusts to the others based on one demand signal, one cadence for decisions, and one set of trade-offs across service, cost, and working capital. Unified planning creates a network that balances real constraints with shared goals, replacing fragmented decision-making.
Aligning what the customer wants and what’s being produced results in higher customer satisfaction, better shelf availability, and less waste. It should drive every decision. An interconnected system also means there’s better data being fed into the machines running the supply chains. Once that foundation is in place, organizations can confidently add in advanced automation and AI without complicating signals.
The best approach is to start by focusing on a few proven use cases rather than chasing dozens of pilots. Starting narrow gives organizations the opportunity to scale quickly because they’ll know that what they have in place works.
Companies that are coming out ahead are redesigning workflows, roles, and governance to support faster decision loops and consistent learning. Leaders are also closing talent gaps with targeted upskilling for planners, schedulers, and analysts, enabling teams to utilize data and automation effectively as the system scales.
Signal coherence turns data into shared understanding and shared understanding into coordinated action. When retailers and manufacturers plan and execute from the same signal, they minimize surprises, reduce waste, and make better decisions faster.
Industry 4.0 began with connected machines. Its future belongs to connected decisions powering supply chains that learn, adapt, and strengthen each time they’re tested.
